Determining an image capture payload burst structure based on a metering image capture sweep

Abstract

A first plurality of images of a scene may be captured. Each image of the first plurality of images may be captured with a different total exposure time (TET). Based at least on the first plurality of images, a TET sequence may be determined for capturing images of the scene. A second plurality of images of the scene may be captured. Images in the second plurality of images may be captured using the TET sequence. Based at least on the second plurality of images, an output image of the scene may be constructed.

What is claimed is: 
     
       1. A method comprising:
 capturing, by an image sensor, a first plurality of images of a scene, wherein each image of the first plurality of images is captured with a different total exposure time (TET); 
 based at least on the first plurality of images, determining a TET sequence for capturing images of the scene; 
 capturing, by the image sensor, a second plurality of images of the scene, wherein images in the second plurality of images are captured using the TET sequence; and 
 based at least on the second plurality of images, constructing an output image of the scene, wherein determining the TET sequence comprises determining that the scene is a low dynamic range (LDR) scene and defining a common value for TETs in the TET sequence, and wherein constructing the output image of the scene comprises aligning and combining one or more of the images in the second plurality of images captured using the common value. 
 
     
     
       2. The method of  claim 1 , wherein determining the TET sequence comprises determining a scene histogram based on at least one of the images in the first plurality of images of the scene. 
     
     
       3. The method of  claim 2 , wherein the scene histogram is based on downsampling and combining the images in the first plurality of images of the scene. 
     
     
       4. The method of  claim 2 , wherein histograms are stored in a training database and are based on at least two images captured of respective scenes, wherein the histograms in the training database are associated with respective dynamic range parameters, wherein the respective dynamic range parameters indicate whether the respective scenes exhibit LDR or high dynamic range (HDR), and wherein determining the TET sequence comprises:
 comparing the scene histogram to at least one histogram in the training database; and 
 based on an outcome of the comparison, determining a dynamic range parameter for the scene from the respective dynamic range parameters, wherein determining the TET sequence is further based on the dynamic range parameter for the scene. 
 
     
     
       5. The method of  claim 4 , wherein the dynamic range parameter for the scene indicates that the scene is LDR, and wherein determining the TET sequence comprises selecting a single TET value to use in the TET sequence. 
     
     
       6. The method of  claim 4 , wherein the histograms in the training database are also associated with respective target pixel values, wherein determining the TET sequence further comprises:
 based on the respective target pixel values in the training database, determining one or more target pixel values for the scene; and 
 based on the one or more determined target pixel values, selecting one or more TET values to use in the TET sequence. 
 
     
     
       7. An article of manufacture including a non-transitory computer-readable medium, having stored thereon program instructions that, upon execution by a computing device, cause the computing device to perform operations comprising:
 capturing, by an image sensor, a first plurality of images of a scene, wherein each image of the first plurality of images is captured with a different total exposure time (TET); 
 based at least on the first plurality of images, determining a TET sequence for capturing images of the scene; 
 capturing, by the image sensor, a second plurality of images of the scene, wherein images in the second plurality of images are captured using the TET sequence; and 
 based at least on the second plurality of images, constructing an output image of the scene, wherein determining the TET sequence comprises determining that the scene is a low dynamic range (LDR) scene and defining a common value for TETs in the TET sequence, and wherein constructing the output image of the scene comprises aligning and combining one or more of the images in the second plurality of images captured using the common value. 
 
     
     
       8. The article of manufacture of  claim 7 , wherein determining the TET sequence comprises determining a scene histogram based on at least one of the images in the first plurality of images of the scene. 
     
     
       9. The article of manufacture of  claim 8 , wherein the scene histogram is based on downsampling and combining the images in the first plurality of images of the scene. 
     
     
       10. The article of manufacture of  claim 8 , wherein histograms are stored in a training database and are based on at least two images captured of respective scenes, wherein the histograms in the training database are associated with respective dynamic range parameters, wherein the respective dynamic range parameters indicate whether the respective scenes exhibit LDR or high dynamic range (HDR), and wherein determining the TET sequence further comprises:
 comparing the scene histogram to at least one of the histograms in the training database; and 
 based on an outcome of the comparison, determining a dynamic range parameter for the scene from the respective dynamic range parameters, wherein determining the TET sequence is further based on the dynamic range parameter for the scene. 
 
     
     
       11. The article of manufacture of  claim 10 , wherein the dynamic range parameter for the scene indicates that the scene is LDR, and wherein determining the TET sequence comprises selecting a single TET value to use in the TET sequence. 
     
     
       12. The article of manufacture of  claim 10 , wherein the histograms in the training database are also associated with respective target pixel values, wherein determining the TET sequence further comprises:
 based on the respective target pixel values in the training database, determining one or more target pixel values for the scene; and 
 based on the one or more determined target pixel values, selecting one or more TET values to use in the TET sequence. 
 
     
     
       13. A computing device comprising:
 at least one processor; 
 an image sensor; 
 data storage; and 
 program instructions, stored in the data storage, that upon execution by the at least one processor cause the computing device to perform operations including: 
 capturing, at the image sensor, a first plurality of images of a scene, wherein each image of the first plurality of images is captured with a different total exposure time (TET); 
 based at least on the first plurality of images, determining a TET sequence for capturing images of the scene; 
 capturing, at the image sensor, a second plurality of images of the scene, wherein images in the second plurality of images are captured using the TET sequence; and 
 based at least on the second plurality of images, constructing an output image of the scene, wherein determining the TET sequence comprises determining that the scene is a low dynamic range (LDR) scene and defining a common value for TETs in the TET sequence, and wherein constructing the output image of the scene comprises aligning and combining one or more of the images in the second plurality of images captured using the common value. 
 
     
     
       14. The computing device of  claim 13 , wherein determining the TET sequence comprises determining a scene histogram based on at least one of the images in the first plurality of images of the scene. 
     
     
       15. The computing device of  claim 14 , wherein the scene histogram is based on downsampling and combining the images in the first plurality of images of the scene. 
     
     
       16. The computing device of  claim 14 , wherein histograms are stored in a training database and are based on at least two images captured of respective scenes, wherein the histograms in the training database are associated with respective dynamic range parameters, wherein the respective dynamic range parameters indicate whether the respective scenes exhibit LDR or high dynamic range (HDR), and wherein determining the TET sequence further comprises:
 comparing the scene histogram to at least one of the histograms in the training database; and 
 based on an outcome of the comparison, determining a dynamic range parameter for the scene from the respective dynamic range parameters, wherein determining the TET sequence is further based on the dynamic range parameter for the scene. 
 
     
     
       17. The computing device of  claim 16 , wherein the dynamic range parameter for the scene indicates that the scene is LDR, and wherein determining the TET sequence comprises selecting a single TET value to use in the TET sequence. 
     
     
       18. The computing device of  claim 16 , wherein the histograms in the training database are also associated with respective target pixel values, wherein determining the TET sequence further comprises:
 based on the respective target pixel values in the training database, determining one or more target pixel values for the scene; and 
 based on the one or more determined target pixel values, selecting one or more TET values to use in the TET sequence.